docstr: Intializes CFs and other related variables.
docstr_tok: ["Intializes","CFs","and","other","related","variables","."]
code: def do_cf_initializations(
      self, total_CFs, algorithm, features_to_vary
  ):
      """Intializes CFs and other related variables."""
  
      self.cf_init_weights = [total_CFs, algorithm, features_to_vary]
  
      if algorithm == "RandomInitCF":
          # no. of times to run the experiment with random inits for diversity
          self.total_random_inits = total_CFs
          self.total_CFs = 1  # size of counterfactual set
      else:
          self.total_random_inits = 0
          self.total_CFs = total_CFs  # size of counterfactual set
  
      # freeze those columns that need to be fixed
      if features_to_vary != self.features_to_vary:
          self.features_to_vary = features_to_vary
      self.feat_to_vary_idxs = (
          self.data_interface.get_indexes_of_features_to_vary(
              features_to_vary=features_to_vary
          )
      )
      self.freezer = tf.constant(
          [
              1.0 if ix in self.feat_to_vary_idxs else 0.0
              for ix in range(len(self.minx[0]))
          ]
      )
  
      # CF initialization
      if len(self.cfs) != self.total_CFs:
          self.cfs = []
          for _ in range(self.total_CFs):
              one_init = [[]]
              for jx in range(self.minx.shape[1]):
                  one_init[0].append(
                      np.random.uniform(
                          self.minx[0][jx], self.maxx[0][jx]
                      )
                  )
              self.cfs.append(tf.Variable(one_init, dtype=tf.float32))
  
imports: import copy
  import random
  import timeit
  import numpy
  import tensorflow
  from dice_ml import diverse_counterfactuals
  from dice_ml.counterfactual_explanations import CounterfactualExplanations
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
label_desc: Use image node_id 5 for calling the DiceTensorFlow2 obj's underlying member method code with example usage: obj.do_cf_initializations(total_CFs, algorithm, features_to_vary) without return types